The work concerns the detailed finite element modelling of multiscale experimental tests performed on glass-Textile Reinforced Mortars (TRM) with the aim of investigating the complex three-dimensional aspects associated with the TRM behavior and understanding what parameters are crucial for the correct simulation and prediction of the composite’s response at each scale. The investigation ranges from materials to structural scale. Each sample’s component and its interfaces are modeled individually. The results show that the 3D micro-modelling successfully replicates the experimental TRM response at different scales by using consistent materials parameters in all the simulation levels, proving a comprehensive understanding of the composite behavior and performance.

Numerical prediction of the mechanical behavior of TRM composites and TRM-strengthened masonry panels / Giordano, E.; Masciotta, M. G.; Clementi, F.; Ghiassi, B.. - In: CONSTRUCTION AND BUILDING MATERIALS. - ISSN 0950-0618. - STAMPA. - 397:(2023), pp. 1-16. [10.1016/j.conbuildmat.2023.132376]

Numerical prediction of the mechanical behavior of TRM composites and TRM-strengthened masonry panels

Giordano, E.
Membro del Collaboration Group
;
Clementi, F.
Membro del Collaboration Group
;
2023-01-01

Abstract

The work concerns the detailed finite element modelling of multiscale experimental tests performed on glass-Textile Reinforced Mortars (TRM) with the aim of investigating the complex three-dimensional aspects associated with the TRM behavior and understanding what parameters are crucial for the correct simulation and prediction of the composite’s response at each scale. The investigation ranges from materials to structural scale. Each sample’s component and its interfaces are modeled individually. The results show that the 3D micro-modelling successfully replicates the experimental TRM response at different scales by using consistent materials parameters in all the simulation levels, proving a comprehensive understanding of the composite behavior and performance.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/319011
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